Kurowski Michal A, Bujnicki Janusz M
Bioinformatics Laboratory, International Institute of Molecular and Cell Biology, Warsaw, Poland.
Nucleic Acids Res. 2003 Jul 1;31(13):3305-7. doi: 10.1093/nar/gkg557.
Rigorous assessments of protein structure prediction have demonstrated that fold recognition methods can identify remote similarities between proteins when standard sequence search methods fail. It has been shown that the accuracy of predictions is improved when refined multiple sequence alignments are used instead of single sequences and if different methods are combined to generate a consensus model. There are several meta-servers available that integrate protein structure predictions performed by various methods, but they do not allow for submission of user-defined multiple sequence alignments and they seldom offer confidentiality of the results. We developed a novel WWW gateway for protein structure prediction, which combines the useful features of other meta-servers available, but with much greater flexibility of the input. The user may submit an amino acid sequence or a multiple sequence alignment to a set of methods for primary, secondary and tertiary structure prediction. Fold-recognition results (target-template alignments) are converted into full-atom 3D models and the quality of these models is uniformly assessed. A consensus between different FR methods is also inferred. The results are conveniently presented on-line on a single web page over a secure, password-protected connection. The GeneSilico protein structure prediction meta-server is freely available for academic users at http://genesilico.pl/meta.
对蛋白质结构预测的严格评估表明，当标准序列搜索方法失效时，折叠识别方法能够识别蛋白质之间的远缘相似性。研究表明，使用优化后的多序列比对而非单序列，并且将不同方法结合以生成一致模型时，预测的准确性会提高。有几个元服务器可整合各种方法进行的蛋白质结构预测，但它们不允许提交用户定义的多序列比对，而且很少对结果保密。我们开发了一种新颖的蛋白质结构预测万维网网关，它结合了其他现有元服务器的有用功能，但输入的灵活性要大得多。用户可以将氨基酸序列或多序列比对提交给一组用于一级、二级和三级结构预测的方法。折叠识别结果（目标-模板比对）会转换为全原子三维模型，并统一评估这些模型的质量。还会推断不同折叠识别方法之间的一致性。结果通过安全的、受密码保护的连接在单个网页上方便地在线呈现。基因硅蛋白质结构预测元服务器可供学术用户在http://genesilico.pl/meta免费使用。
